people:gang:regression_i

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+ | <WRAP center box> | ||

+ | ####Math 531 Regression I.####\\ ###Fall 2015### | ||

+ | </WRAP> | ||

+ | ~~META:title=Math 531 Regression I.~~ | ||

+ | * **Instructor:** [[people:gang:]] | ||

+ | * **Email:** <gang@math.binghamton.edu> | ||

+ | * **Phone number:** (607) 777-3550 | ||

+ | * **Office:** OW-133 | ||

+ | * **Meeting time & location: ** MWF 1:10 - 2:10pm at OW 100E. | ||

+ | * **Office hours: ** MW 3:30-5:00pm or by appointment.\\ If you need to reach me, please e-mail <gang@math.binghamton.edu>.\\ **__Please include [Math531] in the subject line of your email, or your email may not be read promptly.__** | ||

+ | |||

+ | |||

+ | ===== Prerequisite ===== | ||

+ | Math 501 and Math 502, or equivalent. A course in linear algebra. **Graduate students from outside of the mathematical department and senior undergraduate students may take this course with Instructor's approval.** | ||

+ | |||

+ | ===== Learning Objectives ===== | ||

+ | - Basic theories of linear regression models: estimation, statistical inference, prediction, model diagnosis,model selection, etc. | ||

+ | - Proficient use of programming language R with applications to regression models. | ||

+ | - Basic training in scientific writing. | ||

+ | - Basic training in presentation. | ||

+ | |||

+ | <WRAP center round important 60%> | ||

+ | This course is a 4-credit course, which means that students are expected to do at least 12.5 hours of course-related work or activity each week during the semester. This includes scheduled class lecture/discussion meeting times as well as time spent completing assigned readings, studying for tests and examinations, preparing written and computing assignments, and other course-related tasks. | ||

+ | </WRAP> | ||

+ | |||

+ | ===== Recommended Texts ===== | ||

+ | |||

+ | The required texts is **Faraway (2014)** (see below for details). | ||

+ | |||

+ | * **Required text** | ||

+ | * Faraway (2014). Linear Models with R, Second Edition. (Chapman & Hall/CRC Texts in Statistical Science) | ||

+ | * Link to R scripts of the book: [[http://www.maths.bath.ac.uk/~jjf23/LMR/scripts2/|R codes]] | ||

+ | | ||

+ | * **Recommended additional reading** | ||

+ | * Sheather (2009). A Modern Approach to Regression with R. (Springer Texts in Statistics) | ||

+ | |||

+ | ===== Software ===== | ||

+ | R is chosen to be the statistical software used in this course. There are many online resources where the students can learn the basics of R. | ||

+ | - [[https://cran.r-project.org/doc/manuals/R-intro.pdf|An Introduction to R]] | ||

+ | - [[http://www.cyclismo.org/tutorial/R/|R tutorial by Kelly Black]] | ||

+ | - Here is a pointer to [[http://www.r-bloggers.com/|R blogs]]. | ||

+ | |||

+ | Please install R before the beginning of the semester. In addition to R, some may find RStudio to be handy. | ||

+ | Downloads: | ||

+ | * [[http://cran.cnr.berkeley.edu/|R]] - mirror hosted at UC Berkeley. | ||

+ | * [[http://www.rstudio.com/products/rstudio/download/|R Studio]] - a more user friendly platform for R. | ||

+ | |||

+ | **Note: This is not an R class. R will not even be taught in class. You are expect to learning R programming by yourself.** | ||

+ | ===== Grading ===== | ||

+ | * **Homework (20%)**: | ||

+ | - Assigned every day. Don't skimp on the homework if you want a good grade. | ||

+ | - You may discuss the problems with each other in general terms, but you must write your own solution. | ||

+ | - All sources, including friends and colleagues, must be cited. | ||

+ | * **Midterm exam (20%)**: October 23rd (tentative, subject to change) | ||

+ | * **Final Exam (30%)**: TBA. | ||

+ | * **Team project (30%)**: [[people:gang:regression_i:requirement|Guidelines]] | ||

+ | ===== Presentation Schedule ===== | ||

+ | Dec. 2nd, 1:10pm - 1:40pm Wenbo Wang, Xin Qi and Xu Chu; | ||

+ | |||

+ | Dec. 2nd, 1:40pm - 2:10pm Ruiqi Liu, Junyi Dong, Lin Yao and Liping Gu; | ||

+ | |||

+ | Dec. 4th, 1:10pm - 1:40pm Changwei Zhou, Hao Xu and Baiyang Qi; | ||

+ | |||

+ | Dec. 4th, 1:40pm - 2:10pm Rachael Kline, Yuan Fang and Rui Gao; | ||

+ | |||

+ | Dec. 7th, 1:10pm - 1:40pm Wenming Deng, Chen Liang, Xiang Li; | ||

+ | |||

+ | Grading points: | ||

+ | |||

+ | Slides (40%): you need to prepare slides that are clear, concise with great visualization of your results; Do not include too much technical detail; | ||

+ | |||

+ | Team presentation performance (30%, graded individually): you need to tell a story about your project; try to be as organized as possible and get right into the points; Your presentation should not be longer than 25 minutes; otherwise, you will be penalized; | ||

+ | |||

+ | Question/Answer performance (30%): you need to reserve 5 minutes for questions. The way you handle all questions must clearly show that you have sufficient knowledge of what you are doing. Otherwise, you will be penalized. I also encourage questions from the crowd, and if you ask a good question I will take a note in my heart :) |

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